AlphaQubit: Google’s research on quantum error correction

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class="article-meta__abstract-text uni-body--large"> Our new AI system accurately identifies errors inside quantum computers, helping to make this new technology more reliable. </p> </div> </div> <div class="article-meta__container"> <div class="article-meta__content"> <div class="article-meta__author"> <div class="article-meta__author-noimage" data-component="uni-monogram" data-author="Google DeepMind and Quantum AI teams"> </div> <div class="article-meta__author-info"> <div class="article-meta__author-name">Google DeepMind and Quantum AI teams</div> </div> </div> </div> <div class="article-share__wrapper" data-analytics-module='{ "module_name": "Hero Share", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class ="uni-social-share uni-social-share--desktop" data-component="uni-social-share-dropdown" > <a aria-label="Share" class="uni-social-share__trigger" role="button" tabindex="0" aria-expanded="false" data-analytics='{ "event": "page 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Keyword:%0A%0AAlphaQubit%20tackles%20one%20of%20quantum%20computing%E2%80%99s%20biggest%20challenges%0A%0AAlphaQubit is an AI-based decoder that identifies quantum computing errors with state-of-the-art accuracy.%0A%0Ahttps://blog.google/technology/google-deepmind/alphaqubit-quantum-error-correction/" target="_blank" data-analytics='{ "event": "page interaction", "category": "social", "action": "share", "label": "email" }' data-ga4-method="email"> <svg aria-hidden="true" class="h-c-icon h-c-icon--social h-c-icon--30px" viewBox="0 0 30 30" > <use xmlns:xlink="http://www.w3.org/1999/xlink" href="/static/blogv2/images/icons.svg?version=pr20241113-1756#social-mail"></use> </svg> <div class="article-share__title">Mail</div> </a> <div class="copy-link uni-copy-share uni-click-tracker" data-component="uni-copy-popup-component" data-analytics='{ "event": "page interaction", "category": "social", "action": "share", "label": "copy" }' data-ga4-analytics-share-copy-link data-ga4-method="Copy 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style="--height-percentage: 41.7%;"> <div class="aspect-ratio-image__container"> <img srcset="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Hero_Image_-_2096_x_1182.width-600.format-webp.webp 600w, https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Hero_Image_-_2096_x_1182.width-1200.format-webp.webp 1200w, https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Hero_Image_-_2096_x_1182.width-1600.format-webp.webp 1600w" sizes="(max-width: 599px) 100vw, (max-width: 1023px) 600px, 1024px" src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Hero_Image_-_2096_x_1182.width-1200.format-webp.webp" fetchpriority="high" alt="Illustration of a qubit as a pink sphere with X, Y, Z axis planes, surrounded by blue qubits on a dark background" class="aspect-ratio-image__image"> </div> </div> </figure> </div> </div> <section class="uni-container article-container"> <div class="uni-wrapper article-container__wrapper"> <div class="uni-content uni-blog-article-container article-container__content " data-reading-time="true" data-component="uni-article-body"> <google-read-aloud-player data-analytics-module='{ "event": "module_impression", "module_name": "ai_audio", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }' data-date-modified="2024-11-20T21:11:00.789987+00:00" data-progress-bar-style="half-wave" data-api-key="AIzaSyBLT6VkYe-x7sWLZI2Ep26-fNkBKgND-Ac" data-article-style="style9" data-tracking-ids="G-HGNBTNCHCQ,G-6NKTLKV14N" data-voice-list="en.ioh-pngnat:Cyan,en.usb-pngnat:Lime" data-layout-style="style1" data-highlight-mode="word-over-paragraph" data-highlight-text-color="#000000" data-highlight-word-background="#8AB4F8" data-highlight-paragraph-background="#D2E3FC" data-background="linear-gradient(180deg, #F1F3F4 0%, #F8F9FA 100%)" data-foreground-color="#202124" data-font="600 16px Google Sans, sans-serif" data-box-shadow="0px 1px 3px 1px rgba(60, 64, 67, 0.15)" style=min-height:55px;display:block;max-width:726px;margin-left:auto;margin-right:auto;margin-bottom:36px; > </google-read-aloud-player> <script async src="https://www.gstatic.com/readaloud/player/web/api/js/api.js"></script>  <div class="module--text module--text__article" data-analytics-module='{ "module_name": "Paragraph", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="uni-paragraph article-paragraph" data-component="uni-article-paragraph"> <div class="rich-text"><p data-block-key="99qp2">Quantum computers have the potential to revolutionize drug discovery, material design and fundamental physics — that is, if we can get them to work reliably.</p><p data-block-key="ecqih">Certain problems, which would take a conventional computer billions of years to solve, would take a quantum computer just hours. However, these new processors are more prone to noise than conventional ones. If we want to make quantum computers more reliable, especially at scale, we need to accurately identify and correct these errors.</p><p data-block-key="fvfj6">In a <a href="https://www.nature.com/articles/s41586-024-08148-8">paper published today in Nature</a>, we introduce AlphaQubit, an AI-based decoder that identifies quantum computing errors with state-of-the-art accuracy. This collaborative work brought together Google DeepMind’s machine learning knowledge and Google Quantum AI’s error correction expertise to accelerate progress on building a reliable quantum computer.</p><p data-block-key="becp9">Accurately identifying errors is a critical step towards making quantum computers capable of performing long computations at scale, opening the doors to scientific breakthroughs and many new areas of discovery.</p></div> </div> </div> <section class="related-content-tout " data-analytics-module='{ "module_name": "Related Content Tout", "section_header": "What is quantum computing?" }'> <a href="https://blog.google/inside-google/googlers/ask-techspert-what-quantum-computing/" class="related-content-tout__wrapper " data-ga4-analytics-cta-click='{ "event": "cta_click", "link_text": "See more" }' data-ga4-analytics-lead-click='{ "event": "article_lead_click", "link_text": "Ask a Techspert: What is quantum computing?", "link_type": "internal", "article_name": "Ask a Techspert: What is quantum computing?", "author_name" : "Peter Schottenfels", "page_name": "ask-techspert-what-quantum-computing", "position": "1 of 1", "click_location": "undefined", "primary_tag": "Series - Ask a Techspert", "secondary_tags": "Inside Google (DEPRECATED),Googlers", "publish_date": "2019-03-04|18:00", "hero_media": "image", "days_since_published": "2091", "content_category": "Announcement", "word_count": "1233", "has_audio": "no", "has_video": "yes", "has_image": "yes", "has_carousel": "no" }' > <div class="related-content-tout__eyebrow related-content-tout__eyebrow--desktop"> <p class="related-content-tout__eyebrow-headline">Related Article</p> </div> <div class="related-content-tout__content-wrapper"> <figure class="related-content-tout__image-container"> <picture> <source media="(max-resolution: 1.5dppx)" sizes="300px" srcset="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Bristlecone_mounted_with_Marissa_.width-300.format-webp.webp 300w"> <source media="(min-resolution: 1.5dppx)" sizes="600px" srcset="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Bristlecone_mounted_with_Marissa_.width-600.format-webp.webp 600w"> <img src="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Bristlecone_mounted_with_Marissa_.width-600.format-webp.webp" alt="Article's hero media" sizes=" 300px, 600px" srcset="https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Bristlecone_mounted_with_Marissa_.width-300.format-webp.webp 300w, https://storage.googleapis.com/gweb-uniblog-publish-prod/images/Bristlecone_mounted_with_Marissa_.width-600.format-webp.webp 600w" data-target="image" loading="lazy" > </picture> </figure> <div class="related-content-tout__content "> <div class="related-content-tout__eyebrow related-content-tout__eyebrow--mobile"> <p class="related-content-tout__eyebrow-headline">Related Article</p> </div> <p class="related-content-tout__title">What is quantum computing?</p> <p class="related-content-tout__text">Learn more in our Ask a Techspert series.</p> <div class="related-content-tout__cta"> <span>See more</span> <svg xmlns="http://www.w3.org/2000/svg" width="16" height="17" viewBox="0 0 16 17" fill="none" class="related-content-tout__cta-arrow"> <path fill-rule="evenodd" clip-rule="evenodd" d="M8 0.503906C3.58162 0.503906 0 4.08553 0 8.50391C0 12.9223 3.58162 16.5042 8 16.5042C12.4184 16.5042 16 12.9223 16 8.50391C16 4.08553 12.4184 0.503906 8 0.503906ZM8 13.3476L7.14387 12.4915L10.526 9.10937H3.15629V7.89844H10.526L7.14387 4.51632L8 3.66019L12.8437 8.50391L8 13.3476Z" fill="#414141"/> </svg> </div> </div> </div> </a> </section> <div class="module--text module--text__article" data-analytics-module='{ "module_name": "Paragraph", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="uni-paragraph article-paragraph" data-component="uni-article-paragraph"> <div class="rich-text"><h2 data-block-key="92crg">Correcting quantum computing errors</h2><p data-block-key="1c0kl">Quantum computers harness the unique properties of matter at the smallest scales, such as superposition and entanglement, to solve certain types of complex problems in far fewer steps than classical computers. The technology relies on qubits, or quantum bits, which can sift through vast sets of possibilities using quantum interference to find an answer.</p><p data-block-key="3pg5d">The natural quantum state of a qubit is fragile and can be disrupted by various factors: microscopic defects in hardware, heat, vibration, electromagnetic interference and even cosmic rays (which are everywhere).</p><p data-block-key="akva1">Quantum error correction offers a way forward by using redundancy: grouping multiple qubits into a single logical qubit, and regularly performing consistency checks on it. The decoder preserves quantum information by using these consistency checks to identify errors in the logical qubit, so they can be corrected.</p></div> </div> </div> <div class="article-image" data-analytics-module='{ "module_name": "Inline Images", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="article-image__is-caption "> <video tabindex="0" autoplay loop muted playsinline class="article-image__media article-image--full" src="https://storage.googleapis.com/gweb-uniblog-publish-prod/original_videos/Figure_1_animation.mp4" type="video/mp4" title="Diagram of a logical qubit's state checked over time, with data passed to a neural network to predict quantum errors" alt="AlphaQubit Figure 1"> Video format not supported </video> </div> <figcaption class="article-image__caption article-image__is-caption-image "><div class="rich-text"><p data-block-key="pzsm7">Here, we illustrate how nine physical qubits (small gray circles) in a qubit grid of side length 3 (code distance) form a logical qubit. At each step, 8 more qubits perform consistency checks (square and semicircle areas, blue and magenta when failing and gray otherwise) at each time step which inform the neural network decoder (AlphaQubit). At the end of the experiment, AlphaQubit determines what errors occurred.</p></div></figcaption> </div> <div class="module--text module--text__article" data-analytics-module='{ "module_name": "Paragraph", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="uni-paragraph article-paragraph" data-component="uni-article-paragraph"> <div class="rich-text"><h2 data-block-key="3rgy9">Creating a neural-network contender for decoding</h2><p data-block-key="673ot">AlphaQubit is a neural-network based decoder drawing on <a href="https://research.google/blog/transformer-a-novel-neural-network-architecture-for-language-understanding/">Transformers</a>, a deep learning architecture developed at Google that underpins many of today’s large language models. Using the consistency checks as an input, its task is to correctly predict whether the logical qubit — when measured at the end of the experiment — has flipped from how it was prepared.</p><p data-block-key="fi530">We began by training our model to decode the data from a set of 49 qubits inside a <a href="https://research.google/blog/suppressing-quantum-errors-by-scaling-a-surface-code-logical-qubit/">Sycamore quantum processor</a>, the central computational unit of the quantum computer. To teach AlphaQubit the general decoding problem, we used a quantum simulator to generate hundreds of millions of examples across a variety of settings and error levels. Then we finetuned AlphaQubit for a specific decoding task by giving it thousands of experimental samples from a particular Sycamore processor.</p><p data-block-key="53vlb">When tested on new Sycamore data, AlphaQubit set a new standard for accuracy when compared with the previous leading decoders. In the largest Sycamore experiments, AlphaQubit makes 6% fewer errors than tensor network methods, <a href="https://www.nature.com/articles/s41586-022-05434-1">which are highly accurate but impractically slow</a>. AlphaQubit also makes 30% fewer errors than <a href="https://arxiv.org/abs/1310.0863">correlated matching</a>, an accurate decoder that is fast enough to scale.</p></div> </div> </div> <div class="article-image" data-analytics-module='{ "module_name": "Inline Images", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="article-image__is-caption "> <img alt="Line chart comparing accuracy of three quantum decoders over distance, with AlphaQubit showing highest accuracy throughout" src=" https://storage.googleapis.com/gweb-uniblog-publish-prod/original_images/Figure_2.gif " class="article-image--full" > </div> <figcaption class="article-image__caption article-image__is-caption-image "><div class="rich-text"><p data-block-key="vpf49">Decoding accuracies for small and large Sycamore experiments (distance 3 = 17 physical qubits, and distance 5 = 49 physical qubits). AlphaQubit is more accurate than the tensor network (TN, a method that is not expected to scale at large experiments) and correlated matching (an accurate decoder with the speed to scale).</p></div></figcaption> </div> <div class="module--text module--text__article" data-analytics-module='{ "module_name": "Paragraph", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="uni-paragraph article-paragraph" data-component="uni-article-paragraph"> <div class="rich-text"><h2 data-block-key="5p9y9">Scaling AlphaQubit for future systems</h2><p data-block-key="d6okk">We expect quantum computers to advance beyond what’s available today. To see how AlphaQubit would adapt to larger devices with lower error levels, we trained it using data from simulated quantum systems of up to 241 qubits, as this exceeded what was available on the Sycamore platform.</p><p data-block-key="5gic8">Again, AlphaQubit outperformed leading algorithmic decoders, suggesting it will also work on mid-sized quantum devices in the future.</p></div> </div> </div> <div class="article-image" data-analytics-module='{ "module_name": "Inline Images", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="article-image__is-caption "> <img alt="Line chart showing accuracy of two decoders improving with distance, to virtually 100% at higher scales, with AlphaQubit best" src=" https://storage.googleapis.com/gweb-uniblog-publish-prod/original_images/Figure_3.gif " class="article-image--full" > </div> <figcaption class="article-image__caption article-image__is-caption-image "><div class="rich-text"><p data-block-key="mkf3p">Decoding accuracies for different scaling/simulated experiments, from distance 3 (17 qubits) to distance 11 (241 qubits). The Tensor Network decoder does not appear in this graph, as it is too slow to run at large distances. The accuracy of the other two decoders increases when increasing distance (that is, when using more physical qubits). At each distance, AlphaQubit is more accurate than correlated matching.</p></div></figcaption> </div> <div class="module--text module--text__article" data-analytics-module='{ "module_name": "Paragraph", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="uni-paragraph article-paragraph" data-component="uni-article-paragraph"> <div class="rich-text"><p data-block-key="5p9y9">Our system also demonstrated advanced features like the ability to accept and report confidence levels on inputs and outputs. These information-rich interfaces can help further improve the performance of the quantum processor.</p><p data-block-key="62p4l">And when we trained AlphaQubit on samples that included up to 25 rounds of error correction, it maintained good performance on simulated experiments of up to 100,000 rounds, showing its ability to generalize to scenarios beyond its training data.</p></div> </div> </div> <div class="module--text module--text__article" data-analytics-module='{ "module_name": "Paragraph", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="uni-paragraph article-paragraph" data-component="uni-article-paragraph"> <div class="rich-text"><h2 data-block-key="5p9y9">Moving towards practical quantum computing</h2><p data-block-key="8gbsc">AlphaQubit represents a major milestone in using machine learning for quantum error correction. But we still face significant challenges involving speed and scalability.</p><p data-block-key="c32ii">For example, each consistency check in a fast superconducting quantum processor is measured a million times every second. While AlphaQubit is great at accurately identifying errors, it’s still too slow to correct errors in a superconducting processor in real time. As quantum computing grows toward the potentially millions of qubits needed for commercially relevant applications, we’ll also need to find more data-efficient ways of training AI-based decoders.</p><p data-block-key="cm2bt">Our teams are combining pioneering advances in machine learning and quantum error correction to overcome these challenges — and pave the way for reliable quantum computers that can tackle some of the world’s most complex problems.</p><p data-block-key="b7c28"><a href="https://www.nature.com/articles/s41586-024-08148-8"><i>Read our paper in Nature</i></a><i>.</i></p></div> </div> </div> <div class="uni-blog-article-tags article-tags" data-analytics-module='{ "module_name": "Article Tags", "section_header": "AlphaQubit tackles one of quantum computing’s biggest challenges" }'> <div class="uni-blog-article-tags__wrapper"> <span class="uni-blog-article-tags__label uni-eyebrow">POSTED IN:</span> </div> <nav class="uni-blog-article-tags__container uni-click-tracker" data-analytics='{ "category": "landing page lead", "action": "article tag" }' > <ul class="uni-blog-article-tags__tags-list"> <li> <a class="uni-blog-article-tags-value uni-body--small uni-link-active" href=" https://blog.google/technology/google-deepmind/ " data-ga4-analytics-landing-lead='{ "event": "landing_page_lead", "link_text": "Google DeepMind" }' data-analytics='{"label": "topics: Google DeepMind"}' > Google DeepMind </a> </li> </ul> </nav> </div> </div> </div> </section> </article> <div class="uni-related-articles-cards ga4-carousel" data-analytics-module='{ "module_name": "Article Footer Related Stories", "section_header": "Related stories" }' > <div class="uni-related-articles-cards-container" data-component="uni-related-articles"> <div class="uni-related-articles-grid-title"> <div class="uni-related-articles-grid-wrapper"> <h3 class="uni-related-articles-cards__title"> <div class="uni-related-articles-cards__title-content "> Related stories </div> </h3> </div> </div> <div class="uni-related-articles-cards__track"> <ul aria-role="menu" aria-label="Related articles" class="uni-related-articles-cards__wrap "> <li class="uni-related-articles-cards__item uni-related-articles-cards__item--img "> <a href="https://blog.google/technology/ai/a-new-era-of-discovery/" class="uni-related-articles-cards__link" data-index="1" data-target="card" data-primaryTag = "topics - 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AlphaQubit: Google’s research on quantum error correction